Common Method Variance Pdf Free
Kanisha Dezarn
In applied statistics, (e.g., applied to the social sciences and psychometrics), common-method variance (CMV) is the spurious "variance that is attributable to the measurement method rather than to the constructs the measures are assumed to represent"[1] or equivalently as "systematic error variance shared among variables measured with and introduced as a function of the same method and/or source".[2] For example, an electronic survey method might influence results for those who might be unfamiliar with an electronic survey interface differently than for those who might be familiar. If measures are affected by CMV or common-method bias, the intercorrelations among them can be inflated or deflated depending upon several factors.[3] Although it is sometimes assumed that CMV affects all variables, evidence suggests that whether or not the correlation between two variables is affected by CMV is a function of both the method and the particular constructs being measured.[4]
Several ex ante remedies exist that help to avoid or minimize possible common method variance. Important remedies have been compiled and discussed by Chang et al. (2010), Lindell & Whitney (2001) and Podsakoff et al. (2003).[5][6][1]
Using simulated data sets, Richardson et al. (2009) investigate three ex post techniques to test for common method variance: the correlational marker technique, the confirmatory factor analysis (CFA) marker technique, and the unmeasured latent method construct (ULMC) technique. Only the CFA marker technique turns out to provide some value, whereas the commonly used Harman test does not turn out to provide such value.[2] A comprehensive example of this technique has been demonstrated by Williams et al. (2010).[7] Kock (2015) discusses a full collinearity test that is successful in the identification of common method bias with a model that nevertheless passes standard convergent and discriminant validity assessment criteria based on a CFA.[8][9]
Cross-sectional studies of attitude-behavior relationships are vulnerable to the inflation of correlations by common method variance (CMV). Here, a model is presented that allows partial correlation analysis to adjust the observed correlations for CMV contamination and determine if conclusions about the statistical and practical significance of a predictor have been influenced by the presence of CMV. This method also suggests procedures for designing questionnaires to increase the precision of this adjustment.
JIBS receives many manuscripts that report findings from analyzing survey data based on same-respondent replies. This can be problematic since same-respondent studies can suffer from common method variance (CMV). Currently, authors who submit manuscripts to JIBS that appear to suffer from CMV are asked to perform validity checks and resubmit their manuscripts. This letter from the Editors is designed to outline the current state of best practice for handling CMV in international business research.
A large number of papers submitted to JIBS use data collected from a survey instrument. When self-report questionnaires are used to collect data at the same time from the same participants, common method variance (CMV) may be a concern. This concern is strongest when both the dependent and focal explanatory variables are perceptual measures derived from the same respondent (Podsakoff & Organ, 1986).
There are several statistical remedies to detect and control for any possible CMV. A post hoc Harman one-factor analysis is often used to check whether variance in the data can be largely attributed to a single factor. Additionally, other statistical procedures can be applied to partial out common factors or to control for them.
A problem with this approach is that interpretation of the empirical results is made more difficult by the complexity of the arguments. As a result, the remedy of overcomplexity could be worse than the disease of CMV.Footnote 6 Basically, adding complexity such as mediating, moderating and/or non-linear effects makes sense only if guided by a good theory. In the end, sound theory that directs design and method is, of course, the bottom line that characterizes all good research, be it survey-based or not.
The fourth remedy is to apply ex post statistical approaches. Indeed, there are quite a few of them; here, we only briefly refer to some of the more popular ones since there are several other papers with more details (please refer to the references attached to this Letter). Perhaps the most common but ineffective response by authors to address CMV (other than ignoring it) is to rely on Harman's single-factor test to assert that their research is not pervasively affected by CMV. This method loads all items from each of the constructs into an exploratory factor analysis to see whether one single factor does emerge or whether one general factor does account for a majority of the covariance between the measures; if not, the claim is that CMV is not a pervasive issue. However, Podsakoff et al. (2003) explain that this claim is likely to be incomplete because Harman's test is insensitive. It is unlikely that a single-factor model will fit the data, and there is no useful guideline as to what would be the acceptable percentage of explained variance of a single-factor model. The JIBS team therefore believes that simply reporting seemingly reassuring outcomes from Harman's single-factor test is insufficient to prove that CMV is not a pervasive issue.
Lindell and Whitney (2001), Podsakoff et al. (2003) and Malhotra, Kim, and Patil (2006) review several statistical methods that are more sophisticated than Harman's test, which can be used to test and possibly control for CMV. Different statistical remedies are available for different types of research settings and different sources of CMV. Promising statistical remedies include a partial correlation procedure and a direct measure of a latent common method factor. The former method partials out the first unrotated factor from the exploratory factor analysis, and then continues to determine whether the theoretical relationships among the variables of interest do still hold. The latter method allows questionnaire items to load on their theoretical constructs, as well as on a latent CMV factor, and examines the significance of theoretical constructs with or without the common factor method. Both methods have their own limitations, however, one of which is the assumption that the sources of CMV can be well identified and validly measured.Footnote 7 A recommended solution is to use multiple remedies, not just one remedy, in order to assuage the various concerns about CMV.
In March 2009, we reviewed all the articles published in JIBS between 2000 and the present for evidence of potential sources of CMV. Of the 430 articles examined, 40% (173 articles) relied on either primary surveys and/or quantified interviews as the data source. The 173 articles were then characterized by potential sources of common methods bias. Almost all the articles (167 articles) contained one or more sources of CMV, and most appeared to have multiple sources. Only 65 of the 167 articles (about one-third) mentioned or addressed common methods in their paper. Of this group, half the articles (32) used Harman's single-factor test or something similar to test for CMV. Fifteen articles used another approach. Only nine articles used both Harman's test and at least one other correction method to control for CMV.
These statistics, of course, tell us only how frequently common methods appear in recently published JIBS articles, not the magnitude of the potential bias from CMV in these articles. Previous research estimating the magnitude of the effects did not include JIBS articles; see for example, Doty and Glick (1998) and Cote and Buckley (1987). So, the most we can say is there may be a problem based on frequency of usage of common methods, but at present we have no estimates of the magnitude of the problem.
Based on this short survey, it appears that common method bias has not been recognized nor addressed by most IB scholars, even in JIBS, the top journal in the field of international business. We recognize, of course, that standards for rigor in empirical work are continually rising. What were acceptable methodological practices even five years ago can easily and rapidly become unacceptable as social science scholars better understand the limitations of their empirical techniques and develop more rigorous methods for identifying and correcting for potential biases in their work. The purpose of our Letter from the Editors is therefore not to criticize earlier research, but rather to encourage IB scholars to implement current best practices in research methods. We argue that the hurdle barrier must now be set higher in JIBS vis à vis CMV. It is time for IB scholars to address, and reduce or offset where feasible, the use of common methods in their empirical work.
The JIBS editors believe this approach has been insufficient. For the current editorial team, it is now standard practice to return a manuscript to the author when it appears to suffer from common method bias and the issue has been ignored in the manuscript. The desk rejection letter asks the author to perform validity checks and resolve any CMV issues before resubmitting the manuscript.
More often than not, a perfect solution is out of reach. If ex ante methods are not doable, the JIBS editors recommend that IB scholars use multiple ex post procedural remedies including possibly a more complex model specification, and partialing out or controlling for CMV (remedies 2, 3 and 4). What we ask for is that CMV-related methodological issues should be discussed carefully and explicitly in any manuscript submitted to JIBS that uses single-respondent data. This will often imply the need to apply a number of the remedies referred to above. While the problems with CMV were not well understood by IB scholars in the past and, as a result, there were many JIBS articles published that might suffer from multiple sources of common methods bias, the standards have changed and IB scholars must adopt current best practices.